An Optimal Combining Attack Strategy Against Economic Dispatch of Integrated Energy System

Zhang, Yunning; Xie, XueXin; Fu, Wenlong; Chen, Xi; Hu, Songlin; Zhang, Lei; Xia, YuHang

doi:10.1109/tcsii.2022.3196931

Cited by 25 publications

(16 citation statements)

References 19 publications

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“…Therefore, in order to maximize the benefits of the district integrated energy system, its planning and operation must be rationally optimized. The energy demand of electricity, cooling and heating loads on a typical summer day in a park is used as a model 30 . The algorithm of reference 5 , the algorithm of reference 6 and the improved genetic algorithm proposed in this paper are used to simulate the regional comprehensive energy system optimization model, and the solution results are shown in Table 1.…”

Section: Case Analysismentioning

confidence: 99%

Research on optimal scheduling strategy of regional integrated energy system based on improved genetic algorithm

Li,

Cheng,

Tang

2023

Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023)

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In order to solve the problems of increasing energy demand, environmental pollution and low efficiency of energy conversion and utilization, a research on optimal scheduling strategy of regional integrated energy system based on an improved genetic algorithm is proposed. First, the optimal dispatch model of the regional integrated energy system is established, and the objective function is decomposed and iteratively solved by the column constraint generation algorithm. Integer coding is used for pipe diameter coding, and simulated annealing penalty function is used to transform constraints. The global optimization capability of the improved genetic algorithm and the local search capability of simulated annealing are complementary and integrated to realize the optimal scheduling of the regional comprehensive energy system. The experimental results show that the operating cost is 55416 CNY without considering the changing working conditions of the equipment; the operating cost when considering the changing working conditions of the equipment is 49388 CNY, which verifies that the established optimal scheduling model is efficient and feasible.

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Section: Case Analysismentioning

confidence: 99%

Research on optimal scheduling strategy of regional integrated energy system based on improved genetic algorithm

Li,

Cheng,

Tang

2023

Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023)

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“…4 of 17 (5) To solve the intermittent problem, the cutoff frequency ϕ bis (t) is rearranged. (6) The extreme value of the signal h(t) = cos ϕ bis (t)dt is taken as the node, x(t) is approximated using B-spline interpolation, and the approximation result m(t) is obtained. (7) It is determined whether the cutoff criterion is met, and the bandwidth threshold ξ is set.…”

Section: Time-varying Filtering-based Empirical Mode Decompositionmentioning

confidence: 99%

“…The progress in wind power research is of mutual scientific value to the development of other renewable energy sources, such as photovoltaic power generation, hydroelectric power, and nuclear energy. With the increasing penetration of smart grids, it is obvious that power forecasts play an essential role in the intelligence of the power grid [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Multistep Wind Power Prediction Using Time-Varying Filtered Empirical Modal Decomposition and Improved Adaptive Sparrow Search Algorithm-Optimized Phase Space Reconstruction–Echo State Network

Tan

Shen

et al. 2023

Sustainability

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Accurate wind power prediction is vital for improving grid stability. In order to improve the accuracy of wind power prediction, in this study, a hybrid prediction model combining time-varying filtered empirical modal decomposition (TVFEMD), improved adaptive sparrow search algorithm (IASSA)-optimized phase space reconstruction (PSR) and echo state network (ESN) methods was proposed. First, the wind power data were decomposed into a set of subsequences by using TVFEMD. Next, PSR was used to construct the corresponding phase space matrix for sequences, which were then divided into training sets, validation sets, and testing sets. Then, ESN was used for subsequence prediction. Finally, the predicted values of all the subseries were used to determine the final predicted power. To enhance the model performance, the sparrow search algorithm was improved in terms of the discoverer position update strategy, the follower position update strategy, and the population structure. IASSA was employed to synchronously optimize multiple parameters of PSR-ESN. The results revealed that the proposed model has higher applicability and prediction accuracy than existing models.

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“…The fault diagnosis of hydropower units is a mapping from the data space or feature space of monitoring signals to the fault space of units. At present, most of the fault-diagnosis methods of hydropower units applied in power stations still rely on the power spectrum and phase spectrum of the measured signals to identify fault states manually [ 15 , 16 ]. This conventional fault-diagnosis method with high human cost is highly dependent on expert knowledge and operation and maintenance experience, which is difficult to meet the needs of intelligent hydropower station construction.…”

Section: Introductionmentioning

confidence: 99%

Axis Orbit Recognition of the Hydropower Unit Based on Feature Combination and Feature Selection

Liu

Zheng

Zhou

et al. 2023

Sensors

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Axis-orbit recognition is an essential means for the fault diagnosis of hydropower units. An axis-orbit recognition method based on feature combination and feature selection is proposed, aiming to solve the problems of the low recognition accuracy, poor robustness, and low efficiency of existing axis-orbit recognition methods. First, various contour, moment, and geometric features of axis orbit samples are extracted from the original data and combined into a multidimensional feature set; then, Random Forest (RF)-Fisher feature selection is applied to realize feature dimensionality reduction; and finally, the selected features are set as the input of the support vector machine (SVM), which is optimized by the gravitational search algorithm (GSA) for axis-orbit recognition. The analytical results show that the proposed method has high recognition efficiency and good robustness while maintaining high accuracy for axis-orbit recognition.

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An Optimal Combining Attack Strategy Against Economic Dispatch of Integrated Energy System

Cited by 25 publications

References 19 publications

Research on optimal scheduling strategy of regional integrated energy system based on improved genetic algorithm

Research on optimal scheduling strategy of regional integrated energy system based on improved genetic algorithm

Multistep Wind Power Prediction Using Time-Varying Filtered Empirical Modal Decomposition and Improved Adaptive Sparrow Search Algorithm-Optimized Phase Space Reconstruction–Echo State Network

Axis Orbit Recognition of the Hydropower Unit Based on Feature Combination and Feature Selection

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